def _calculate_agg_features(self, features, entity_frames):
test_feature = features[0]
entity = test_feature.entity
child_entity = test_feature.base_features[0].entity
assert entity.id in entity_frames and child_entity.id in entity_frames
frame = entity_frames[entity.id]
base_frame = entity_frames[child_entity.id]
# Sometimes approximate features get computed in a previous filter frame
# and put in the current one dynamically,
# so there may be existing features here
features = [f for f in features if f.get_name() not in frame.columns]
if not len(features):
return frame
# handle where clause for all functions below
where = test_feature.where
if where is not None:
base_frame = base_frame[base_frame[where.get_name()]]
relationship_path = self.entityset.find_backward_path(
entity.id, child_entity.id)
groupby_var = Relationship._get_link_variable_name(relationship_path)
# if the use_previous property exists on this feature, include only the
# instances from the child entity included in that Timedelta
use_previous = test_feature.use_previous
if use_previous and not base_frame.empty:
# Filter by use_previous values
time_last = self.time_last
if use_previous.is_absolute():
time_first = time_last - use_previous
ti = child_entity.time_index
if ti is not None:
base_frame = base_frame[base_frame[ti] >= time_first]
else:
n = use_previous.value
def last_n(df):
return df.iloc[-n:]
base_frame = base_frame.groupby(groupby_var,
observed=True,
sort=False).apply(last_n)
to_agg = {}
agg_rename = {}
to_apply = set()
# apply multivariable and time-dependent features as we find them, and
# save aggregable features for later
for f in features:
if _can_agg(f):
variable_id = f.base_features[0].get_name()
if variable_id not in to_agg:
to_agg[variable_id] = []
func = f.get_function()
funcname = func
if callable(func):
funcname = func.__name__
to_agg[variable_id].append(func)
# this is used below to rename columns that pandas names for us
agg_rename[u"{}-{}".format(variable_id,
funcname)] = f.get_name()
continue
to_apply.add(f)
# Apply the non-aggregable functions generate a new dataframe, and merge
# it with the existing one
if len(to_apply):
wrap = agg_wrapper(to_apply, self.time_last)
# groupby_var can be both the name of the index and a column,
# to silence pandas warning about ambiguity we explicitly pass
# the column (in actuality grouping by both index and group would
# work)
to_merge = base_frame.groupby(base_frame[groupby_var],
observed=True,
sort=False).apply(wrap)
to_merge.reset_index(1, drop=True, inplace=True)
frame = pd.merge(left=frame,
right=to_merge,
left_index=True,
right_index=True,
how='left')
# Apply the aggregate functions to generate a new dataframe, and merge
# it with the existing one
if len(to_agg):
# groupby_var can be both the name of the index and a column,
# to silence pandas warning about ambiguity we explicitly pass
# the column (in actuality grouping by both index and group would
# work)
to_merge = base_frame.groupby(base_frame[groupby_var],
observed=True,
sort=False).agg(to_agg)
# rename columns to the correct feature names
to_merge.columns = [
agg_rename["-".join(x)] for x in to_merge.columns.ravel()
]
to_merge = to_merge[list(agg_rename.values())]
# workaround for pandas bug where categories are in the wrong order
# see: https://github.com/pandas-dev/pandas/issues/22501
if pdtypes.is_categorical_dtype(frame.index):
categories = pdtypes.CategoricalDtype(
categories=frame.index.categories)
to_merge.index = to_merge.index.astype(object).astype(
categories)
frame = pd.merge(left=frame,
right=to_merge,
left_index=True,
right_index=True,
how='left')
# Handle default values
# 1. handle non scalar default values
iterfeats = [
f for f in features if hasattr(f.default_value, '__iter__')
]
for f in iterfeats:
nulls = pd.isnull(frame[f.get_name()])
for ni in nulls[nulls].index:
frame.at[ni, f.get_name()] = f.default_value
# 2. handle scalars default values
fillna_dict = {
f.get_name(): f.default_value
for f in features if f not in iterfeats
}
frame.fillna(fillna_dict, inplace=True)
# convert boolean dtypes to floats as appropriate
# pandas behavior: https://github.com/pydata/pandas/issues/3752
for f in features:
if (not f.expanding and f.variable_type == variable_types.Numeric
and frame[f.get_name()].dtype.name in ['object', 'bool']):
frame[f.get_name()] = frame[f.get_name()].astype(float)
return frame
def _calculate_agg_features(self, features, entity_frames):
test_feature = features[0]
use_previous = test_feature.use_previous
base_features = test_feature.base_features
where = test_feature.where
entity = test_feature.entity
child_entity = base_features[0].entity
assert entity.id in entity_frames and child_entity.id in entity_frames
index_var = entity.index
frame = entity_frames[entity.id]
base_frame = entity_frames[child_entity.id]
# Sometimes approximate features get computed in a previous filter frame
# and put in the current one dynamically,
# so there may be existing features here
features = [f for f in features if f.get_name() not in frame.columns]
if not len(features):
return
# handle where clause for all functions below
if where is not None:
base_frame = base_frame[base_frame[where.get_name()]]
relationship_path = self.entityset.find_backward_path(
entity.id, child_entity.id)
groupby_var = Relationship._get_link_variable_name(relationship_path)
# if the use_previous property exists on this feature, include only the
# instances from the child entity included in that Timedelta
if use_previous and not base_frame.empty:
# Filter by use_previous values
time_last = self.time_last
if use_previous.is_absolute():
time_first = time_last - use_previous
ti = child_entity.time_index
if ti is not None:
base_frame = base_frame[base_frame[ti] >= time_first]
else:
n = use_previous.value
def last_n(df):
return df.iloc[-n:]
base_frame = base_frame.groupby(groupby_var).apply(last_n)
if not base_frame.empty:
if groupby_var not in base_frame:
# This occured sometimes. I think it might have to do with category
# but not sure. TODO: look into when this occurs
no_instances = True
# if the foreign key column in the child (base_frame) that links to
# frame is an integer and the id column in the parent is an object or
# category dtype, the .isin() call errors.
elif (frame[index_var].dtype != base_frame[groupby_var].dtype
or frame[index_var].dtype.name.find('category') > -1):
try:
frame_as_obj = frame[index_var].astype(object)
base_frame_as_obj = base_frame[groupby_var].astype(object)
except ValueError:
msg = u"Could not join {}.{} (dtype={}) with {}.{} (dtype={})"
raise ValueError(
msg.format(entity.id, index_var,
frame[index_var].dtype, child_entity.id,
groupby_var, base_frame[groupby_var].dtype))
else:
no_instances = check_no_related_instances(
frame_as_obj.values, base_frame_as_obj.values)
else:
no_instances = check_no_related_instances(
frame[index_var].values, base_frame[groupby_var].values)
if base_frame.empty or no_instances:
for f in features:
set_default_column(entity_frames[entity.id], f)
return
def wrap_func_with_name(func, name):
def inner(x):
return func(x)
inner.__name__ = name
return inner
to_agg = {}
agg_rename = {}
to_apply = set()
# apply multivariable and time-dependent features as we find them, and
# save aggregable features for later
for f in features:
if _can_agg(f):
variable_id = f.base_features[0].get_name()
if variable_id not in to_agg:
to_agg[variable_id] = []
func = f.get_function()
# make sure function names are unique
random_id = str(uuid.uuid1())
func = wrap_func_with_name(func, random_id)
funcname = random_id
to_agg[variable_id].append(func)
agg_rename[u"{}-{}".format(variable_id,
funcname)] = f.get_name()
continue
to_apply.add(f)
# Apply the non-aggregable functions generate a new dataframe, and merge
# it with the existing one
if len(to_apply):
wrap = agg_wrapper(to_apply, self.time_last)
# groupby_var can be both the name of the index and a column,
# to silence pandas warning about ambiguity we explicitly pass
# the column (in actuality grouping by both index and group would
# work)
to_merge = base_frame.groupby(base_frame[groupby_var]).apply(wrap)
to_merge.reset_index(1, drop=True, inplace=True)
frame = pd.merge(left=frame,
right=to_merge,
left_on=index_var,
right_index=True,
how='left')
# Apply the aggregate functions to generate a new dataframe, and merge
# it with the existing one
# Do the [variables] accessor on to_merge because the agg call returns
# a dataframe with columns that contain the dataframes we want
if len(to_agg):
# groupby_var can be both the name of the index and a column,
# to silence pandas warning about ambiguity we explicitly pass
# the column (in actuality grouping by both index and group would
# work)
to_merge = base_frame.groupby(base_frame[groupby_var]).agg(to_agg)
# we apply multiple functions to each column, creating
# a multiindex as the column
# rename the columns to a concatenation of the two indexes
to_merge.columns = [
u"{}-{}".format(n1, n2) for n1, n2 in to_merge.columns.ravel()
]
# to enable a rename
to_merge = to_merge.rename(columns=agg_rename)
variables = agg_rename.values()
to_merge = to_merge[variables]
frame = pd.merge(left=frame,
right=to_merge,
left_on=index_var,
right_index=True,
how='left')
# Handle default values
# 1. handle non scalar default values
iterfeats = [
f for f in features if hasattr(f.default_value, '__iter__')
]
for f in iterfeats:
nulls = pd.isnull(frame[f.get_name()])
for ni in nulls[nulls].index:
frame.at[ni, f.get_name()] = f.default_value
# 2. handle scalars default values
fillna_dict = {
f.get_name(): f.default_value
for f in features if f not in iterfeats
}
frame.fillna(fillna_dict, inplace=True)
# convert boolean dtypes to floats as appropriate
# pandas behavior: https://github.com/pydata/pandas/issues/3752
for f in features:
if (not f.expanding and f.variable_type == variable_types.Numeric
and frame[f.get_name()].dtype.name in ['object', 'bool']):
frame[f.get_name()] = frame[f.get_name()].astype(float)
entity_frames[entity.id] = frame
def _calculate_agg_features(self, features, entity_frames):
test_feature = features[0]
use_previous = test_feature.use_previous
base_features = test_feature.base_features
where = test_feature.where
entity = test_feature.entity
child_entity = base_features[0].entity
assert entity.id in entity_frames and child_entity.id in entity_frames
index_var = entity.index
frame = entity_frames[entity.id]
base_frame = entity_frames[child_entity.id]
# Sometimes approximate features get computed in a previous filter frame
# and put in the current one dynamically,
# so there may be existing features here
features = [f for f in features if f.get_name()
not in frame.columns]
if not len(features):
return frame
# handle where clause for all functions below
if where is not None:
base_frame = base_frame[base_frame[where.get_name()]]
relationship_path = self.entityset.find_backward_path(entity.id,
child_entity.id)
groupby_var = Relationship._get_link_variable_name(relationship_path)
# if the use_previous property exists on this feature, include only the
# instances from the child entity included in that Timedelta
if use_previous and not base_frame.empty:
# Filter by use_previous values
time_last = self.time_last
if use_previous.is_absolute():
time_first = time_last - use_previous
ti = child_entity.time_index
if ti is not None:
base_frame = base_frame[base_frame[ti] >= time_first]
else:
n = use_previous.value
def last_n(df):
return df.iloc[-n:]
base_frame = base_frame.groupby(groupby_var).apply(last_n)
if not base_frame.empty:
if groupby_var not in base_frame:
# This occured sometimes. I think it might have to do with category
# but not sure. TODO: look into when this occurs
no_instances = True
# if the foreign key column in the child (base_frame) that links to
# frame is an integer and the id column in the parent is an object or
# category dtype, the .isin() call errors.
elif (frame[index_var].dtype != base_frame[groupby_var].dtype or
frame[index_var].dtype.name.find('category') > -1):
try:
frame_as_obj = frame[index_var].astype(object)
base_frame_as_obj = base_frame[groupby_var].astype(object)
except ValueError:
msg = u"Could not join {}.{} (dtype={}) with {}.{} (dtype={})"
raise ValueError(msg.format(entity.id, index_var,
frame[index_var].dtype,
child_entity.id, groupby_var,
base_frame[groupby_var].dtype))
else:
no_instances = check_no_related_instances(
frame_as_obj.values, base_frame_as_obj.values)
else:
no_instances = check_no_related_instances(
frame[index_var].values, base_frame[groupby_var].values)
if base_frame.empty or no_instances:
for f in features:
set_default_column(entity_frames[entity.id], f)
return frame
def wrap_func_with_name(func, name):
def inner(x):
return func(x)
inner.__name__ = name
return inner
to_agg = {}
agg_rename = {}
to_apply = set()
# apply multivariable and time-dependent features as we find them, and
# save aggregable features for later
for f in features:
if _can_agg(f):
variable_id = f.base_features[0].get_name()
if variable_id not in to_agg:
to_agg[variable_id] = []
func = f.get_function()
# make sure function names are unique
random_id = str(uuid.uuid1())
func = wrap_func_with_name(func, random_id)
funcname = random_id
to_agg[variable_id].append(func)
agg_rename[u"{}-{}".format(variable_id, funcname)] = \
f.get_name()
continue
to_apply.add(f)
# Apply the non-aggregable functions generate a new dataframe, and merge
# it with the existing one
if len(to_apply):
wrap = agg_wrapper(to_apply, self.time_last)
# groupby_var can be both the name of the index and a column,
# to silence pandas warning about ambiguity we explicitly pass
# the column (in actuality grouping by both index and group would
# work)
to_merge = base_frame.groupby(base_frame[groupby_var]).apply(wrap)
to_merge.reset_index(1, drop=True, inplace=True)
frame = pd.merge(left=frame, right=to_merge,
left_on=index_var, right_index=True, how='left')
# Apply the aggregate functions to generate a new dataframe, and merge
# it with the existing one
# Do the [variables] accessor on to_merge because the agg call returns
# a dataframe with columns that contain the dataframes we want
if len(to_agg):
# groupby_var can be both the name of the index and a column,
# to silence pandas warning about ambiguity we explicitly pass
# the column (in actuality grouping by both index and group would
# work)
to_merge = base_frame.groupby(base_frame[groupby_var]).agg(to_agg)
# we apply multiple functions to each column, creating
# a multiindex as the column
# rename the columns to a concatenation of the two indexes
to_merge.columns = [u"{}-{}".format(n1, n2)
for n1, n2 in to_merge.columns.ravel()]
# to enable a rename
to_merge = to_merge.rename(columns=agg_rename)
variables = list(agg_rename.values())
to_merge = to_merge[variables]
frame = pd.merge(left=frame, right=to_merge,
left_on=index_var, right_index=True, how='left')
# Handle default values
# 1. handle non scalar default values
iterfeats = [f for f in features
if hasattr(f.default_value, '__iter__')]
for f in iterfeats:
nulls = pd.isnull(frame[f.get_name()])
for ni in nulls[nulls].index:
frame.at[ni, f.get_name()] = f.default_value
# 2. handle scalars default values
fillna_dict = {f.get_name(): f.default_value for f in features
if f not in iterfeats}
frame.fillna(fillna_dict, inplace=True)
# convert boolean dtypes to floats as appropriate
# pandas behavior: https://github.com/pydata/pandas/issues/3752
for f in features:
if (not f.expanding and
f.variable_type == variable_types.Numeric and
frame[f.get_name()].dtype.name in ['object', 'bool']):
frame[f.get_name()] = frame[f.get_name()].astype(float)
return frame
